Sends an online processing request to a Document OCR processor and parses the response. Extracts and prints full text, page dimensions, detected languages, paragraphs, blocks, lines, and tokens.
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For detailed documentation that includes this code sample, see the following:
Code sample
Java
For more information, see the Document AI Java API reference documentation.
To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.
import com.google.cloud.documentai.v1beta3.Document;
import com.google.cloud.documentai.v1beta3.DocumentProcessorServiceClient;
import com.google.cloud.documentai.v1beta3.DocumentProcessorServiceSettings;
import com.google.cloud.documentai.v1beta3.ProcessRequest;
import com.google.cloud.documentai.v1beta3.ProcessResponse;
import com.google.cloud.documentai.v1beta3.RawDocument;
import com.google.protobuf.ByteString;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeoutException;
public class ProcessOcrDocument {
public static void processOcrDocument()
throws IOException, InterruptedException, ExecutionException, TimeoutException {
// TODO(developer): Replace these variables before running the sample.
String projectId = "your-project-id";
String location = "your-project-location"; // Format is "us" or "eu".
String processerId = "your-processor-id";
String filePath = "path/to/input/file.pdf";
processOcrDocument(projectId, location, processerId, filePath);
}
public static void processOcrDocument(
String projectId, String location, String processorId, String filePath)
throws IOException, InterruptedException, ExecutionException, TimeoutException {
// Initialize client that will be used to send requests. This client only needs
// to be created
// once, and can be reused for multiple requests. After completing all of your
// requests, call
// the "close" method on the client to safely clean up any remaining background
// resources.
String endpoint = String.format("%s-documentai.googleapis.com:443", location);
DocumentProcessorServiceSettings settings =
DocumentProcessorServiceSettings.newBuilder().setEndpoint(endpoint).build();
try (DocumentProcessorServiceClient client = DocumentProcessorServiceClient.create(settings)) {
// The full resource name of the processor, e.g.:
// projects/project-id/locations/location/processor/processor-id
// You must create new processors in the Cloud Console first
String name =
String.format("projects/%s/locations/%s/processors/%s", projectId, location, processorId);
// Read the file.
byte[] imageFileData = Files.readAllBytes(Paths.get(filePath));
// Convert the image data to a Buffer and base64 encode it.
ByteString content = ByteString.copyFrom(imageFileData);
RawDocument document =
RawDocument.newBuilder().setContent(content).setMimeType("application/pdf").build();
// Configure the process request.
ProcessRequest request =
ProcessRequest.newBuilder().setName(name).setRawDocument(document).build();
// Recognizes text entities in the PDF document
ProcessResponse result = client.processDocument(request);
Document documentResponse = result.getDocument();
System.out.println("Document processing complete.");
// Read the text recognition output from the processor
// For a full list of Document object attributes,
// please reference this page:
// https://googleapis.dev/java/google-cloud-document-ai/latest/index.html
// Get all of the document text as one big string
String text = documentResponse.getText();
System.out.printf("Full document text: '%s'\n", escapeNewlines(text));
// Read the text recognition output from the processor
List<Document.Page> pages = documentResponse.getPagesList();
System.out.printf("There are %s page(s) in this document.\n", pages.size());
for (Document.Page page : pages) {
System.out.printf("Page %d:\n", page.getPageNumber());
printPageDimensions(page.getDimension());
printDetectedLanguages(page.getDetectedLanguagesList());
printParagraphs(page.getParagraphsList(), text);
printBlocks(page.getBlocksList(), text);
printLines(page.getLinesList(), text);
printTokens(page.getTokensList(), text);
}
}
}
private static void printPageDimensions(Document.Page.Dimension dimension) {
String unit = dimension.getUnit();
System.out.printf(" Width: %.1f %s\n", dimension.getWidth(), unit);
System.out.printf(" Height: %.1f %s\n", dimension.getHeight(), unit);
}
private static void printDetectedLanguages(
List<Document.Page.DetectedLanguage> detectedLangauges) {
System.out.println(" Detected languages:");
for (Document.Page.DetectedLanguage detectedLanguage : detectedLangauges) {
String languageCode = detectedLanguage.getLanguageCode();
float confidence = detectedLanguage.getConfidence();
System.out.printf(" %s (%.2f%%)\n", languageCode, confidence * 100.0);
}
}
private static void printParagraphs(List<Document.Page.Paragraph> paragraphs, String text) {
System.out.printf(" %d paragraphs detected:\n", paragraphs.size());
Document.Page.Paragraph firstParagraph = paragraphs.get(0);
String firstParagraphText = getLayoutText(firstParagraph.getLayout().getTextAnchor(), text);
System.out.printf(" First paragraph text: %s\n", escapeNewlines(firstParagraphText));
Document.Page.Paragraph lastParagraph = paragraphs.get(paragraphs.size() - 1);
String lastParagraphText = getLayoutText(lastParagraph.getLayout().getTextAnchor(), text);
System.out.printf(" Last paragraph text: %s\n", escapeNewlines(lastParagraphText));
}
private static void printBlocks(List<Document.Page.Block> blocks, String text) {
System.out.printf(" %d blocks detected:\n", blocks.size());
Document.Page.Block firstBlock = blocks.get(0);
String firstBlockText = getLayoutText(firstBlock.getLayout().getTextAnchor(), text);
System.out.printf(" First block text: %s\n", escapeNewlines(firstBlockText));
Document.Page.Block lastBlock = blocks.get(blocks.size() - 1);
String lastBlockText = getLayoutText(lastBlock.getLayout().getTextAnchor(), text);
System.out.printf(" Last block text: %s\n", escapeNewlines(lastBlockText));
}
private static void printLines(List<Document.Page.Line> lines, String text) {
System.out.printf(" %d lines detected:\n", lines.size());
Document.Page.Line firstLine = lines.get(0);
String firstLineText = getLayoutText(firstLine.getLayout().getTextAnchor(), text);
System.out.printf(" First line text: %s\n", escapeNewlines(firstLineText));
Document.Page.Line lastLine = lines.get(lines.size() - 1);
String lastLineText = getLayoutText(lastLine.getLayout().getTextAnchor(), text);
System.out.printf(" Last line text: %s\n", escapeNewlines(lastLineText));
}
private static void printTokens(List<Document.Page.Token> tokens, String text) {
System.out.printf(" %d tokens detected:\n", tokens.size());
Document.Page.Token firstToken = tokens.get(0);
String firstTokenText = getLayoutText(firstToken.getLayout().getTextAnchor(), text);
System.out.printf(" First token text: %s\n", escapeNewlines(firstTokenText));
Document.Page.Token lastToken = tokens.get(tokens.size() - 1);
String lastTokenText = getLayoutText(lastToken.getLayout().getTextAnchor(), text);
System.out.printf(" Last token text: %s\n", escapeNewlines(lastTokenText));
}
// Extract shards from the text field
private static String getLayoutText(Document.TextAnchor textAnchor, String text) {
if (textAnchor.getTextSegmentsList().size() > 0) {
int startIdx = (int) textAnchor.getTextSegments(0).getStartIndex();
int endIdx = (int) textAnchor.getTextSegments(0).getEndIndex();
return text.substring(startIdx, endIdx);
}
return "[NO TEXT]";
}
private static String escapeNewlines(String s) {
return s.replace("\n", "\\n").replace("\r", "\\r");
}
}Node.js
For more information, see the Document AI Node.js API reference documentation.
To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION'; // Format is 'us' or 'eu'
// const processorId = 'YOUR_PROCESSOR_ID'; // Create processor in Cloud Console
// const filePath = '/path/to/local/pdf';
const {DocumentProcessorServiceClient} =
require('@google-cloud/documentai').v1beta3;
// Instantiates a client
const client = new DocumentProcessorServiceClient();
async function processDocument() {
// The full resource name of the processor, e.g.:
// projects/project-id/locations/location/processor/processor-id
// You must create new processors in the Cloud Console first
const name = `projects/${projectId}/locations/${location}/processors/${processorId}`;
// Read the file into memory.
const fs = require('fs').promises;
const imageFile = await fs.readFile(filePath);
// Convert the image data to a Buffer and base64 encode it.
const encodedImage = Buffer.from(imageFile).toString('base64');
const request = {
name,
rawDocument: {
content: encodedImage,
mimeType: 'application/pdf',
},
};
// Recognizes text entities in the PDF document
const [result] = await client.processDocument(request);
console.log('Document processing complete.');
// Read the text recognition output from the processor
// For a full list of Document object attributes,
// please reference this page: https://googleapis.dev/nodejs/documentai/latest/index.html
const {document} = result;
const {text} = document;
// Read the text recognition output from the processor
console.log(`Full document text: ${JSON.stringify(text)}`);
console.log(`There are ${document.pages.length} page(s) in this document.`);
for (const page of document.pages) {
console.log(`Page ${page.pageNumber}`);
printPageDimensions(page.dimension);
printDetectedLanguages(page.detectedLanguages);
printParagraphs(page.paragraphs, text);
printBlocks(page.blocks, text);
printLines(page.lines, text);
printTokens(page.tokens, text);
}
}
const printPageDimensions = dimension => {
console.log(` Width: ${dimension.width}`);
console.log(` Height: ${dimension.height}`);
};
const printDetectedLanguages = detectedLanguages => {
console.log(' Detected languages:');
for (const lang of detectedLanguages) {
const code = lang.languageCode;
const confPercent = lang.confidence * 100;
console.log(` ${code} (${confPercent.toFixed(2)}% confidence)`);
}
};
const printParagraphs = (paragraphs, text) => {
console.log(` ${paragraphs.length} paragraphs detected:`);
const firstParagraphText = getText(paragraphs[0].layout.textAnchor, text);
console.log(
` First paragraph text: ${JSON.stringify(firstParagraphText)}`
);
const lastParagraphText = getText(
paragraphs[paragraphs.length - 1].layout.textAnchor,
text
);
console.log(
` Last paragraph text: ${JSON.stringify(lastParagraphText)}`
);
};
const printBlocks = (blocks, text) => {
console.log(` ${blocks.length} blocks detected:`);
const firstBlockText = getText(blocks[0].layout.textAnchor, text);
console.log(` First block text: ${JSON.stringify(firstBlockText)}`);
const lastBlockText = getText(
blocks[blocks.length - 1].layout.textAnchor,
text
);
console.log(` Last block text: ${JSON.stringify(lastBlockText)}`);
};
const printLines = (lines, text) => {
console.log(` ${lines.length} lines detected:`);
const firstLineText = getText(lines[0].layout.textAnchor, text);
console.log(` First line text: ${JSON.stringify(firstLineText)}`);
const lastLineText = getText(
lines[lines.length - 1].layout.textAnchor,
text
);
console.log(` Last line text: ${JSON.stringify(lastLineText)}`);
};
const printTokens = (tokens, text) => {
console.log(` ${tokens.length} tokens detected:`);
const firstTokenText = getText(tokens[0].layout.textAnchor, text);
console.log(` First token text: ${JSON.stringify(firstTokenText)}`);
const firstTokenBreakType = tokens[0].detectedBreak.type;
console.log(` First token break type: ${firstTokenBreakType}`);
const lastTokenText = getText(
tokens[tokens.length - 1].layout.textAnchor,
text
);
console.log(` Last token text: ${JSON.stringify(lastTokenText)}`);
const lastTokenBreakType = tokens[tokens.length - 1].detectedBreak.type;
console.log(` Last token break type: ${lastTokenBreakType}`);
};
// Extract shards from the text field
const getText = (textAnchor, text) => {
if (!textAnchor.textSegments || textAnchor.textSegments.length === 0) {
return '';
}
// First shard in document doesn't have startIndex property
const startIndex = textAnchor.textSegments[0].startIndex || 0;
const endIndex = textAnchor.textSegments[0].endIndex;
return text.substring(startIndex, endIndex);
};
Python
For more information, see the Document AI Python API reference documentation.
To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.
from typing import Optional, Sequence
from google.api_core.client_options import ClientOptions
from google.cloud import documentai
# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_PROCESSOR_LOCATION" # Format is "us" or "eu"
# processor_id = "YOUR_PROCESSOR_ID" # Create processor before running sample
# processor_version = "rc" # Refer to https://cloud.google.com/document-ai/docs/manage-processor-versions for more information
# file_path = "/path/to/local/pdf"
# mime_type = "application/pdf" # Refer to https://cloud.google.com/document-ai/docs/file-types for supported file types
def process_document_ocr_sample(
project_id: str,
location: str,
processor_id: str,
processor_version: str,
file_path: str,
mime_type: str,
) -> None:
# Optional: Additional configurations for Document OCR Processor.
# For more information: https://cloud.google.com/document-ai/docs/enterprise-document-ocr
process_options = documentai.ProcessOptions(
ocr_config=documentai.OcrConfig(
enable_native_pdf_parsing=True,
enable_image_quality_scores=True,
enable_symbol=True,
# OCR Add Ons https://cloud.google.com/document-ai/docs/ocr-add-ons
premium_features=documentai.OcrConfig.PremiumFeatures(
compute_style_info=True,
enable_math_ocr=False, # Enable to use Math OCR Model
enable_selection_mark_detection=True,
),
)
)
# Online processing request to Document AI
document = process_document(
project_id,
location,
processor_id,
processor_version,
file_path,
mime_type,
process_options=process_options,
)
text = document.text
print(f"Full document text: {text}\n")
print(f"There are {len(document.pages)} page(s) in this document.\n")
for page in document.pages:
print(f"Page {page.page_number}:")
print_page_dimensions(page.dimension)
print_detected_languages(page.detected_languages)
print_blocks(page.blocks, text)
print_paragraphs(page.paragraphs, text)
print_lines(page.lines, text)
print_tokens(page.tokens, text)
if page.symbols:
print_symbols(page.symbols, text)
if page.image_quality_scores:
print_image_quality_scores(page.image_quality_scores)
if page.visual_elements:
print_visual_elements(page.visual_elements, text)
def print_page_dimensions(dimension: documentai.Document.Page.Dimension) -> None:
print(f" Width: {str(dimension.width)}")
print(f" Height: {str(dimension.height)}")
def print_detected_languages(
detected_languages: Sequence[documentai.Document.Page.DetectedLanguage],
) -> None:
print(" Detected languages:")
for lang in detected_languages:
print(f" {lang.language_code} ({lang.confidence:.1%} confidence)")
def print_blocks(blocks: Sequence[documentai.Document.Page.Block], text: str) -> None:
print(f" {len(blocks)} blocks detected:")
first_block_text = layout_to_text(blocks[0].layout, text)
print(f" First text block: {repr(first_block_text)}")
last_block_text = layout_to_text(blocks[-1].layout, text)
print(f" Last text block: {repr(last_block_text)}")
def print_paragraphs(
paragraphs: Sequence[documentai.Document.Page.Paragraph], text: str
) -> None:
print(f" {len(paragraphs)} paragraphs detected:")
first_paragraph_text = layout_to_text(paragraphs[0].layout, text)
print(f" First paragraph text: {repr(first_paragraph_text)}")
last_paragraph_text = layout_to_text(paragraphs[-1].layout, text)
print(f" Last paragraph text: {repr(last_paragraph_text)}")
def print_lines(lines: Sequence[documentai.Document.Page.Line], text: str) -> None:
print(f" {len(lines)} lines detected:")
first_line_text = layout_to_text(lines[0].layout, text)
print(f" First line text: {repr(first_line_text)}")
last_line_text = layout_to_text(lines[-1].layout, text)
print(f" Last line text: {repr(last_line_text)}")
def print_tokens(tokens: Sequence[documentai.Document.Page.Token], text: str) -> None:
print(f" {len(tokens)} tokens detected:")
first_token_text = layout_to_text(tokens[0].layout, text)
first_token_break_type = tokens[0].detected_break.type_.name
print(f" First token text: {repr(first_token_text)}")
print(f" First token break type: {repr(first_token_break_type)}")
if tokens[0].style_info:
print_style_info(tokens[0].style_info)
last_token_text = layout_to_text(tokens[-1].layout, text)
last_token_break_type = tokens[-1].detected_break.type_.name
print(f" Last token text: {repr(last_token_text)}")
print(f" Last token break type: {repr(last_token_break_type)}")
if tokens[-1].style_info:
print_style_info(tokens[-1].style_info)
def print_symbols(
symbols: Sequence[documentai.Document.Page.Symbol], text: str
) -> None:
print(f" {len(symbols)} symbols detected:")
first_symbol_text = layout_to_text(symbols[0].layout, text)
print(f" First symbol text: {repr(first_symbol_text)}")
last_symbol_text = layout_to_text(symbols[-1].layout, text)
print(f" Last symbol text: {repr(last_symbol_text)}")
def print_image_quality_scores(
image_quality_scores: documentai.Document.Page.ImageQualityScores,
) -> None:
print(f" Quality score: {image_quality_scores.quality_score:.1%}")
print(" Detected defects:")
for detected_defect in image_quality_scores.detected_defects:
print(f" {detected_defect.type_}: {detected_defect.confidence:.1%}")
def print_style_info(style_info: documentai.Document.Page.Token.StyleInfo) -> None:
"""
Only supported in version `pretrained-ocr-v2.0-2023-06-02`
"""
print(f" Font Size: {style_info.font_size}pt")
print(f" Font Type: {style_info.font_type}")
print(f" Bold: {style_info.bold}")
print(f" Italic: {style_info.italic}")
print(f" Underlined: {style_info.underlined}")
print(f" Handwritten: {style_info.handwritten}")
print(
f" Text Color (RGBa): {style_info.text_color.red}, {style_info.text_color.green}, {style_info.text_color.blue}, {style_info.text_color.alpha}"
)
def print_visual_elements(
visual_elements: Sequence[documentai.Document.Page.VisualElement], text: str
) -> None:
"""
Only supported in version `pretrained-ocr-v2.0-2023-06-02`
"""
checkboxes = [x for x in visual_elements if "checkbox" in x.type]
math_symbols = [x for x in visual_elements if x.type == "math_formula"]
if checkboxes:
print(f" {len(checkboxes)} checkboxes detected:")
print(f" First checkbox: {repr(checkboxes[0].type)}")
print(f" Last checkbox: {repr(checkboxes[-1].type)}")
if math_symbols:
print(f" {len(math_symbols)} math symbols detected:")
first_math_symbol_text = layout_to_text(math_symbols[0].layout, text)
print(f" First math symbol: {repr(first_math_symbol_text)}")
def process_document(
project_id: str,
location: str,
processor_id: str,
processor_version: str,
file_path: str,
mime_type: str,
process_options: Optional[documentai.ProcessOptions] = None,
) -> documentai.Document:
# You must set the `api_endpoint` if you use a location other than "us".
client = documentai.DocumentProcessorServiceClient(
client_options=ClientOptions(
api_endpoint=f"{location}-documentai.googleapis.com"
)
)
# The full resource name of the processor version, e.g.:
# `projects/{project_id}/locations/{location}/processors/{processor_id}/processorVersions/{processor_version_id}`
# You must create a processor before running this sample.
name = client.processor_version_path(
project_id, location, processor_id, processor_version
)
# Read the file into memory
with open(file_path, "rb") as image:
image_content = image.read()
# Configure the process request
request = documentai.ProcessRequest(
name=name,
raw_document=documentai.RawDocument(content=image_content, mime_type=mime_type),
# Only supported for Document OCR processor
process_options=process_options,
)
result = client.process_document(request=request)
# For a full list of `Document` object attributes, reference this page:
# https://cloud.google.com/document-ai/docs/reference/rest/v1/Document
return result.document
def layout_to_text(layout: documentai.Document.Page.Layout, text: str) -> str:
"""
Document AI identifies text in different parts of the document by their
offsets in the entirety of the document"s text. This function converts
offsets to a string.
"""
# If a text segment spans several lines, it will
# be stored in different text segments.
return "".join(
text[int(segment.start_index) : int(segment.end_index)]
for segment in layout.text_anchor.text_segments
)
What's next
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